Welcome to the Eventual blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
Daft v0.7.15: Safe Type Conversions, Flight Shuffle Optimizations, and PostgreSQL Support
Daft v0.7.15 ships with try_cast for safe type conversion, Flight shuffle LZ4 compression, UUIDv7 timestamp extraction, and PostgreSQL support.
Fall 2025 Review: OSS Updates | UDFs, Functions, & daft.File
Daft Fall 2025: AI Functions, improved UDFs, faster vLLM inference, and new daft.File VideoFile subtype - plus Bigtable sink and Common Crawl loader.
Cutting LLM Batch Inference Time in Half: Dynamic Prefix Bucketing at Scale
Learn how Dynamic Prefix Bucketing reduces LLM batch inference time, improves throughput, and unlocks faster multimodal processing at scale.
Simplifying Voice AI Analytics with Daft: Transcription, Summaries, and Embeddings at Scale
Build a Voice AI analytics pipeline with Daft and Faster-Whisper to convert raw audio into searchable transcripts, summaries, and embeddings at scale.
Using PyTorch DataLoaders to Streamline Multimodal Data
Learn how PyTorch's DataLoader streamlines deep learning pipelines by efficiently loading and shuffling data in batches.
Benchmarks for Multimodal AI: Spark, Ray Data, and Daft
Multimodal AI workloads break traditional data engines. Daft ran 2-7x faster than Ray Data and 4-18x faster than Spark while finishing jobs reliably across audio, video, document, and image workloads.
Introducing Flotilla: Simplifying Multimodal Data Processing at Scale
Flotilla, Daft's new distributed engine, processes terabytes of multimodal data in a single query up to 18x faster than Spark and Ray Data, while running efficiently, reliably, and without manual tuning.
Exploring Daft's Local Execution: The Swordfish Engine
Explore how Daft's Rust-powered engine executes DataFrame and SQL queries. Learn how Swordfish enables fast, streaming image processing at scale.
After the First Run
Using Daft's observability tools to uncover performance pitfalls
Making GPUs Zoom (Part 1)
How Daft is approaching large-scale model inference with advanced GPU optimizations for faster multimodal AI workloads